The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Computer vision-based concrete crack detection using U-net fully convolutional networks
Abstract
Abstract For the first time, U-Net is adopted to detect the concrete cracks in the present study. Focal loss function is selected as the evaluation function, and the Adam algorithm is applied for optimization. The trained U-Net is able of identifying the crack locations from the input raw images under various conditions (such as illumination, messy background, width of cracks, etc.) with high effectiveness and robustness. In addition, U-Net based concrete crack detection method proposed in the present study is compared with the DCNN-based method, and U-Net is found to be more elegant than DCNN with more robustness, more effectiveness and more accurate detection. Furthermore, by examining the fundamental parameters representing the performance of the method, the present U-Net is found to reach higher accuracy with smaller training set than the previous FCNs.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
(2010) · International Conference on Machine Learning · Cited by 13,995
(2006) · Springer eBooks · Cited by 8,123
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,680
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Beck, A., & Teboulle, M. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2028250 · Cited by 2,004
2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(2001) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001 CVPR-01 · doi:10.1109/cvpr.2001.990916 · Cited by 1,876
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,503
Cited by
Machine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
Dais, D., Bal, İ. E., Smyrou, E., & Sarhosis, V. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103606 · Cited by 429
Kheradmandi, N., & Mehranfar, V. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.126162 · Cited by 426
Machine learning in concrete science: applications, challenges, and best practices
Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388
A cost effective solution for pavement crack inspection using cameras and deep neural networks
Mei, Q., & Gül, M. (2020) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2020.119397 · Cited by 253
Metadata
- Authors
- Liu, Z., Cao, Y., Wang, Y., & Wang, W.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 104
- Pages
- 129-139
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W2941356554
- Cited by
- 833
- References
- 31
- FWCI
- 53.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019). Computer vision-based concrete crack detection using U-net fully convolutional networks. Automation in Construction. https://doi.org/10.1016/j.autcon.2019.04.005
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